1887
High resolution geophysics and non-destructive testing for archaeology and monumental heritage
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

The study focuses on the integrated use of multiscale and multisensor remote sensing techniques and big data analysis for the identification of buried archaeological remains or areas of potential archaeological interest. Satellite multispectral data (at very high and high resolution), drone based visible, multispectral, and thermal imagery, and geophysical prospecting (gradiometer) were used. The ancient city of Metaponto was chosen as case study, as it was a very important city in the formative panorama of Italian and it also is one of the most important and best preserved archaeological sites in southern Italy. The analysis of remote sensing data from different sensors, with different resolutions, and referable to different physical parameters, allowed to deepen archaeological knowledge on a landscape scale, as well as on a site scale, going from the analysis of traces of the ancient landscape (e.g. palaeo-channels, canalisation system, main roads), to the discovery of small features (e.g. secondary roads, houses, facilities).

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2024-01-02
2026-01-13

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References

  1. Abate, N., A.Elfadaly, N.Masini, and R.Lasaponara. 2020. Multitemporal 2016–2018 sentinel-2 data enhancement for landscape archaeology: The case study of the Foggia province, southern Italy. Remote Sensing12: 1309. doi:10.3390/rs12081309.
     https://doi.org/10.3390/rs12081309
  2. Abate, N., A.Frisetti, F.Marazzi, N.Masini, and R.Lasaponara. 2021. Multitemporal–multispectral UAS surveys for archaeological research: The case study of San Vincenzo Al Volturno (Molise, Italy). Remote Sensing13: 2719. doi:10.3390/rs13142719.
     https://doi.org/10.3390/rs13142719 [Google Scholar]
  3. Abate, N., and R.Lasaponara. 2019. Preventive archaeology based on open remote sensing data and tools: The cases of Sant’Arsenio (SA) and foggia (FG), Italy. Sustainability11: 4145. doi:10.3390/su11154145.
     https://doi.org/10.3390/su11154145 [Google Scholar]
  4. Abate, N., D.Roubis, V.Vitale, M.Sileo, F.Sogliani, N.Masini, and R.Lasaponara. 2022. Integrated use of multi-temporal multi-sensor and multiscale Remote Sensing data for the understanding of archaeological contexts: The case study of Metaponto, Basilicata. Journal of Physics: Conference Series2204: 012020. doi:10.1088/1742‑6596/2204/1/012020.
     https://doi.org/10.1088/1742-6596/2204/1/012020 [Google Scholar]
  5. Adamesteanu, D., D.Mertens, and F.D’Andria. 1975. Metaponto I. Roma, Italy: Accademia Nazionale dei Lincei.
  6. Agapiou, A., D.Hadjimitsis, and D.Alexakis. 2012. Evaluation of broadband and narrowband vegetation indices for the identification of archaeological crop marks. Remote Sensing4: 3892–3919. doi:10.3390/rs4123892.
     https://doi.org/10.3390/rs4123892
  7. Agapiou, A., D.G.Hadjimitsis, A.Sarris, A.Georgopoulos, and D.D.Alexakis. 2013. Optimum temporal and spectral window for monitoring crop marks over archaeological remains in the Mediterranean region. Journal of Archaeological Science40: 1479–1492. doi:10.1016/j.jas.2012.10.036.
     https://doi.org/10.1016/j.jas.2012.10.036
  8. Agapiou, A., D.G.Hadjimitsis, K.Themistocleous, G.Papadavid, and L.Toulios. 2010. Detection of archaeological crop marks in Cyprus using vegetation indices from Landsat TM/ETM+ satellite images and field spectroscopy measurements. In Presented at the remote sensing, Toulouse, France, eds. U.Michel, and D.L.Civco, 78310V. doi:10.1117/12.864935
     https://doi.org/10.1117/12.864935 [Google Scholar]
  9. Agapiou, A., V.Lysandrou, R.Lasaponara, N.Masini, and G.G.Hadjimitsis. 2016. Study of the variations of archaeological marks at neolithic site of lucera, Italy using high-resolution multispectral datasets. Remote Sensing8: 723. doi:10.3390/rs8090723.
     https://doi.org/10.3390/rs8090723
  10. Aiazzi, B., L.Alparone, S.Baronti, A.Garzelli, and M.Selva. 2013. Spie proceedings. Proc SPIE8892: 0889203. doi:10.1117/12.2030560.
     https://doi.org/10.1117/12.2030560
  11. Alcaras, E., C.Parente, and A.Vallario. 2021. Automation of Pan-sharpening methods for pléiades images using GIS basic functions. Remote Sensing13: 1550. doi:10.3390/rs13081550.
     https://doi.org/10.3390/rs13081550
  12. Amani, M., A.Ghorbanian, S.A.Ahmadi, M.Kakooei, A.Moghimi, S.M.Mirmazloumi, S.H.A.Moghaddam, et al.2020. Google earth engine cloud computing platform for remote sensing Big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing13: 5326–5350. doi:10.1109/JSTARS.2020.3021052.
     https://doi.org/10.1109/JSTARS.2020.3021052 [Google Scholar]
  13. Barrile, V., E.Bernardo, A.Fotia, and G.Bilotta. 2022. Integration of laser scanner, ground-penetrating radar, 3D models and mixed reality for artistic, archaeological and cultural heritage dissemination. Heritage5: 1529–1550. doi:10.3390/heritage5030080.
     https://doi.org/10.3390/heritage5030080 [Google Scholar]
  14. Beck, A.2007. Archaeological site detection: The importance of contrast. Presented at the Proceedings of the 2007 Annual Conference of the Remote Sensing and Photogrammetry Society, Newcastle Upon Tyne, UK, 11–14 September 2007, ISPRS, pp. 307–312.
  15. Bertelli, G.2005. L’insediamento medievale di Torre di Mare (Metaponto) e i suoi rapporti con il territorio. Primi dati. In Presented at the I congresso nazionale di archeologia medievale: auditorium del centro studi della cassa di risparmio di Pisa (ex benedettine), Pisa, 29–31 May 1997, ed. S.Gelichi, 200–205.
     [Google Scholar]
  16. Bertelli, G., and D.Roubis. 2002. Torre di Mare 1. Ricerche Archeologiche nell’Insediamento Medievale di Metaponto (1995–1999). Bari, Italy: Adda.
  17. Calleja, J.F., O.Requejo Pagés, N.Díaz-Álvarez, J.Peón, N.Gutiérrez, E.Martín-Hernández, A.Cebada Relea, D.Rubio Melendi, and P.Fernández Álvarez. 2018. Detection of buried archaeological remains with the combined use of satellite multispectral data and UAV data. International Journal of Applied Earth Observation and Geoinformation73: 555–573. doi:10.1016/j.jag.2018.07.023.
     https://doi.org/10.1016/j.jag.2018.07.023
  18. Campana, S., M.Dabas, L.Marasco, S.Piro, and D.Zamuner. 2009. Integration of remote sensing, geophysical surveys and archaeological excavation for the study of a medieval mound (Tuscany, Italy) Archaeol. Prospect16: 167–176.
     [Google Scholar]
  19. Campana, S., and M.Forte. eds. 2006. From space to place: 2nd International Conference on Remote Sensing in Archaeology: proceedings of the 2nd international workshop, CNR, Rome, Italy, December 4-7, 2006, BAR international series. Presented at the international conference on remote sensing in archaeology, Archeopress, Oxford. Consiglio nazionale delle ricerche (Italy)
  20. Campana, S., and R.Franchovich. 2003. Landscape archaeology in Tuscany: cultural resource management, remotely sensed techniques, GIS based data integration and interpretation. In Presented at the reconstruction of archaeological landscapes through digital technologies, Proceedings of the 1st Italy-United States Workshop, Boston, Massachusetts, USA (November 1–3 2001), eds. M.Forte, and P.R.Williams, 15–28. Oxford, UK: Oxford University Press.
     [Google Scholar]
  21. Capozzoli, L., G.Romano, M.Sileo, R.Lasaponara, J.Bastante, D.Sieczkowska, and N.Masini. 2022. New results from archaeogeophysical investigations in Machu Picchu. In Machu Picchu in context, eds. M.Ziółkowski, N.Masini, and J.M.Bastante, 265–300. Cham: Springer International Publishing. doi:10.1007/978‑3‑030‑92766‑0_7
     https://doi.org/10.1007/978-3-030-92766-0_7 [Google Scholar]
  22. Cerra, D., A.Agapiou, R.M.Cavalli, and A.Sarris. 2018. An objective assessment of hyperspectral indicators for the detection of buried archaeological relics. Remote Sensing10: 500. doi:10.3390/rs10040500.
     https://doi.org/10.3390/rs10040500
  23. Chuvieco, E., M.P.Martín, and A.Palacios. 2002. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing23: 5103–5110. doi:10.1080/01431160210153129.
     https://doi.org/10.1080/01431160210153129
  24. Corrado, G., G.Aiello, D.Barra, P.Di Leo, D.Gioia, M.A.Antonio, R.Parisi, and M.Schiattarella. 2022. Late Quaternary evolution of the Metaponto coastal plain, southern Italy, inferred from geomorphological and borehole data. Quaternary International, 638–639, 84–110. doi:10.1016/j.quaint.2022.01.008.
     https://doi.org/10.1016/j.quaint.2022.01.008 [Google Scholar]
  25. D’Andria, F.1975. Metaponto romana, in: atti XV conv.M.grecia. Presented at the XV Conv.M.Grecia, Napoli, 539–544.
     [Google Scholar]
  26. Diara, F., and F.Rinaudo. 2018. Open Source HBIM for Cultural Heritage: A Project Proposal XLII-2, 303–309. doi:10.5194/isprs‑archives‑XLII‑2‑303‑2018
     https://doi.org/10.5194/isprs-archives-XLII-2-303-2018
  27. Elfadaly, A., M.Abate, N.Masini, and R.Lasaponara. 2020. Sar sentinel 1 imaging and detection of palaeo-landscape features in the Mediterranean area. Remote Sensing12: 2611. doi:10.3390/rs12162611.
     https://doi.org/10.3390/rs12162611
  28. Elfadaly, A., K.Abutaleb, D.M.Naguib, and R.Lasaponara. 2022. Detecting the environmental risk on the archaeological sites using satellite imagery in Basilicata Region, Italy. The Egyptian Journal of Remote Sensing and Space Science25: 181–193. doi:10.1016/j.ejrs.2022.01.007.
     https://doi.org/10.1016/j.ejrs.2022.01.007
  29. Estornell, J., J.M.Martí-Gavliá, M.T.Sebastiá, and J.Mengual. 2013. Principal component analysis applied to remote sensing. Modelling in Science Education and Learning6: 83. doi:10.4995/msel.2013.1905.
     https://doi.org/10.4995/msel.2013.1905 [Google Scholar]
  30. Fattore, C., N.Abate, F.Faridani, N.Masini, and R.Lasaponara. 2021. Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: The case study of flood and fire mapping in metaponto, Italy. Sensors21: 1791. doi:10.3390/s21051791.
     https://doi.org/10.3390/s21051791 [Google Scholar]
  31. Fedi, M., F.Cella, G.Florio, M.La Manna, and V.Paoletti. 2017. Geomagnetometry for archaeology. In Sensing the past, eds. N.Masini, and F.Soldovieri, 203–230. Berlin, Heidelberg: Springer.
     [Google Scholar]
  32. Forte, M., and S.R.L.Campana2016. Digital methods and remote sensing in archaeology: Archaeology in the Age of sensing, quantitative methods in the humanities and social sciences. Springer International Publishing. doi:10.1007/978‑3‑319‑40658‑9
     https://doi.org/10.1007/978-3-319-40658-9
  33. Giannotta, M.T.1980. Metaponto ellenistico-romana. Galatina.: Congedo Editore.
  34. Giardino, L.1982. Metaponto tardo-imperiale e Turiostu: Proposta di identificazione in margine ad un miliarium di Giuliano l’Apostata. Studi Antich3: 155–173.
  35. Giardino, L.1991. Grumentum e Metaponto. Due esempi di passaggio dal tardoantico all'alto medioevo in Basilicata. Mélanges de L'Ecole Française de Rome. Moyen-Age, Temps Modernes103: 827–858. doi:10.3406/mefr.1991.3202.
     https://doi.org/10.3406/mefr.1991.3202
  36. Gioia, D., M.Bavusi, et al.2017. A geoarchaeological study of the Metaponto coastal belt, Southern Italy, based on geomorphological mapping and GIS-supported classification of landforms. Geogr. Fis. E Din. Quat, 137–148. doi:10.4461/GFDQ.2016.39.13.
     https://doi.org/10.4461/GFDQ.2016.39.13
  37. Gioia, D., M.Bavusi, P.Di Leo, T.Giammatteo, and M.Schiattarella. 2020. Geoarchaeology and geomorphology of the Metaponto area, Ionian coastal belt, Italy. Journal of Maps16: 117–125. doi:10.1080/17445647.2019.1701575.
     https://doi.org/10.1080/17445647.2019.1701575 [Google Scholar]
  38. Holata, L., J.Plzák, R.Světlík, and J.Fonte. 2018. Integration of Low-resolution ALS and ground-based SfM photogrammetry data. A cost-effective approach providing an ‘enhanced 3D model’ of the hound Tor archaeological landscapes (Dartmoor, South-West England). Remote Sensing10: 1357. doi:10.3390/rs10091357.
     https://doi.org/10.3390/rs10091357
  39. Jensen, J.R.2016. Introductory digital image processing: A remote sensing perspective, Pearson series in geographic information science. Glenview, IL: Pearson Education, Inc.
  40. Khalaf, N., and T.Insoll. 2019. Monitoring Islamic archaeological landscapes in Ethiopia using open source satellite imagery. Journal of Field Archaeology44: 401–419. doi:10.1080/00934690.2019.1629256.
     https://doi.org/10.1080/00934690.2019.1629256
  41. Kumar, L., and O.Mutanga. 2018. Google earth engine applications since inception: usage, trends, and potential. Remote Sensing10: 1509. doi:10.3390/rs10101509.
     https://doi.org/10.3390/rs10101509
  42. Lacava, M.1891. Topografia e Storia di Metaponto. Napoli, Italy: Morano.
  43. Larson, D., C.Lipo, and E.Ambos. 2003. Application of advanced geophysical methods and engineering principles in an emerging scientific archaeology: Environmental Geoscience. Undefined.
  44. Lasaponara, R., N.Abate, and N.Masini. 2022. On the Use of google earth engine and sentinel data to detect “lost” sections of ancient roads. The case of Via Appia. IEEE Geoscience and Remote Sensing Letters, doi:10.1109/LGRS.2021.3054168.
     https://doi.org/10.1109/LGRS.2021.3054168 [Google Scholar]
  45. Lasaponara, R., and N.Masini. 2006. Identification of archaeological buried remains based on the normalized difference vegetation index (NDVI) from Quickbird satellite data. IEEE Geoscience and Remote Sensing Letters3: 325–328. doi:10.1109/LGRS.2006.871747.
     https://doi.org/10.1109/LGRS.2006.871747 [Google Scholar]
  46. Lasaponara, R., and N.Masini. 2006. On the potential of QuickBird data for archaeological prospection. International Journal of Remote Sensing27: 3607–3614. doi:10.1080/01431160500333983.
     https://doi.org/10.1080/01431160500333983
  47. Lasaponara, R., and N.Masini. 2007. Detection of archaeological crop marks by using satellite QuickBird multispectral imagery. Journal of Archaeological Science34: 214–221. doi:10.1016/j.jas.2006.04.014.
     https://doi.org/10.1016/j.jas.2006.04.014
  48. Liss, B., M.D.Howland, and T.E.Levy. 2017. Testing Google Earth Engine for the automatic identification and vectorization of archaeological features: A case study from Faynan, Jordan. Journal of Archaeological Science: Reports15: 299–304. doi:10.1016/j.jasrep.2017.08.013.
     https://doi.org/10.1016/j.jasrep.2017.08.013 [Google Scholar]
  49. Loncan, L., L.B.de Almeida, J.M.Bioucas-Dias, X.Briottet, J.Chanussot, N.Dobigeon, S.Fabre, W.Liao, et al.2015. Hyperspectral pansharpening: A review. IEEE Geoscience and Remote Sensing Magazine3: 27–46. doi:10.1109/MGRS.2015.2440094.
     https://doi.org/10.1109/MGRS.2015.2440094 [Google Scholar]
  50. Luo, L., X.Wang, H.Guo, R.Lasaponara, X.Zong, N.Masini, G.Wang, et al.2019. Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment232: 111280. doi:10.1016/j.rse.2019.111280.
     https://doi.org/10.1016/j.rse.2019.111280
  51. Masini, N., and R.Lasaponara. 2006. Satellite-based recognition of landscape archaeological features related to ancient human transformation. Journal of Geophysics and Engineering3: 230–235. doi:10.1088/1742‑2132/3/3/004.
     https://doi.org/10.1088/1742-2132/3/3/004
  52. Masini, N., and F.Soldovieri. 2017. Sensing the Past: From artifact to historical site, Geotechnologies and the Environment. Cham: Springer International Publishing. doi:10.1007/978‑3‑319‑50518‑3
     https://doi.org/10.1007/978-3-319-50518-3
  53. Mutanga, O., and L.Kumar. 2019. Google earth engine applications. Remote Sensing11: 591. doi:10.3390/rs11050591.
     https://doi.org/10.3390/rs11050591
  54. Orengo, H.A., F.C.Conesa, A.Garcia-Molsosa, A.Lobo, A.S.Green, M.Madella, and C.A.Petrie. 2020. Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data. Proceedings of the National Academy of Sciences117: 18240–18250. doi:10.1073/pnas.2005583117.
     https://doi.org/10.1073/pnas.2005583117 [Google Scholar]
  55. Parcak, S.H.2009. Satellite remote sensing for archaeology. London, New York: Routledge.
  56. Parcak, S.H.2014. GIS, remote sensing, and landscape archaeology. Oxford University Press. doi:10.1093/oxfordhb/9780199935413.013.11
     https://doi.org/10.1093/oxfordhb/9780199935413.013.11
  57. Persico, R.2014. Introduction to ground penetrating radar: inverse scattering and data processing. Hoboken, NJ: IEEE Press.
  58. Roubis, D.2012. Ricognizioni infrasito a Santa Maria d’Anglona (Tursi –Mt). In ΑΜΦΙ ΣΙΡΙΟΣ ΡΟΑΣ. nuove ricerche Su eraclea e La siritide, eds. M.Osanna, and G.Zuchtriegel. osanna edizioni, 291–304. Venosa, Italy.
     [Google Scholar]
  59. Roubis, D.2015. Archeologia dei paesaggi a Pandosia (S.M. d’Anglona). Siris15: 163–176.
  60. Rouse, J., R.H.Haas, D.Deering, J.A.Schell, and J.Harlan. 1973. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor]. undefined.
  61. Rowlands, A., and A.Sarris. 2007. Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing. Journal of Archaeological Science34: 795–803. doi:10.1016/j.jas.2006.06.018.
     https://doi.org/10.1016/j.jas.2006.06.018
  62. Stott, D., D.Boyd, A.Beck, and A.Cohn. 2015. Airborne LiDAR for the detection of archaeological vegetation marks using biomass as a proxy. Remote Sensing7: 1594–1618. doi:10.3390/rs70201594.
     https://doi.org/10.3390/rs70201594
  63. Štular, B., S.Eichert, and E.Lozić. 2021. Airborne LiDAR point cloud processing for archaeology. pipeline and QGIS toolbox. Remote Sensing13: 3225. doi:10.3390/rs13163225.
     https://doi.org/10.3390/rs13163225 [Google Scholar]
  64. Tucker, C.J.1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment8: 127–150. doi:10.1016/0034‑4257(79)90013‑0.
     https://doi.org/10.1016/0034-4257(79)90013-0
  65. White, D.C., M.Williams, and S.L.Barr. 2008. Detecting Sub-surface soil disturbance using hyperspectral first derivative band ratios of associated vegetation stress. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences37: 243–248.
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Integrated use of GIS and remote sensing techniques for landscape-scale archaeological analysis: the case study of Metaponto, Basilicata, Italy
Exploration Geophysics 55, 51 (2024); https://doi.org/10.1080/08123985.2023.2242885
/content/journals/10.1080/08123985.2023.2242885
/content/journals/10.1080/08123985.2023.2242885
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  • Article Type: Research Article
Keyword(s): archaeology; geophysics; GIS; remote sensing; satellite; UAS

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